Many business applications involve adversarial relationships in which both sides adapt their strategies to optimize their opposing benefits. One of the key characteristics of these applications is the wide range of strategies that an adversary may choose as they adapt their strategy dynamically to sustain benefits and evade authorities. In this paper, we present a novel way of approaching these types of applications, in particular in the context of Anti-Money Laundering. We provide a mechanism through which diverse, realistic and new unobserved behavior may be generated to discover potential unobserved adversarial actions to enable organizations to preemptively mitigate these risks. In this regard, we make three main contributions. (a) Propose a novel behavior-based model as opposed to individual transactions-based models currently used by financial institutions. We introduce behavior traces as enriched relational representation to represent observed human behavior. (b) A modelling approach that observes these traces and is able to accurately infer the goals of actors by classifying the behavior into money laundering or standard behavior despite significant unobserved activity. And (c) a synthetic behavior simulator that can generate new previously unseen traces. The simulator incorporates a high level of flexibility in the behavioral parameters so that we can challenge the detection algorithm. Finally, we provide experimental results that show that the learning module (automated investigator) that has only partial observability can still successfully infer the type of behavior, and thus the simulated goals, followed by customers based on traces - a key aspiration for many applications today.

This paper presents a novel approach for automated analysis of process models discovered using process mining techniques. Process mining explores underlying processes hidden in the event data generated by various devices. Our proposed Inductive machine learning method was used to build business process models based on actual event log data obtained from a hotel's Property Management System (PMS). The PMS can be considered as a Multi Agent System (MAS) because it is integrated with a variety of external systems and IoT devices. Collected event log combines data on guests stay recorded by hotel staff, as well as data streams captured from telephone exchange and other external IoT devices. Next, we performed automated analysis of the discovered process models using formal methods. Spin model checker was used to simulate process model executions and automatically verify the process model. We proposed an algorithm for the automatic transformation of the discovered process model into a verification model. Additionally, we developed a generator of positive and negative examples. In the verification stage, we have also used Linear temporal logic (LTL) to define requested system specifications. We find that the analysis results will be well suited for process model repair.

The market research report on the Global Deep Learning Market has been formulated through a series of extensive primary and secondary research approaches. The data is further verified and validated by industry experts and professionals. The forecast for 2020-2027 has been covered in the report and offers an extensive historical analysis for the key segments of the Deep Learning market. The well-formulated research report aims to provide the readers with a better understanding of the industry and help them formulate strategic investment plans. The report also evaluates the market dynamics, including drivers, restraints, opportunities, threats, challenges, and other key segments.

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The global Machine learning Market to grow at a CAGR of 42% during the forecast period, according to the latest report. Machine learning extracts meaningful insights from raw data to quickly solve complex, data-rich business problems. Machine learning in business helps in enhancing business scalability and improving business operations for companies across the globe. Benefits of Machine Learning is Customer Lifetime Value Prediction, Predictive Maintenance, Eliminates Manual Data Entry, Detecting Spam, Product Recommendations, Financial Analysis, Image Recognition, Medical Diagnosis, Improving Cyber Security AND Increasing Customer Satisfaction. A comprehensive analysis of global Machine learning Market has recently added by Market research Inc to its vast repository.

With help from high resolution satellite imagery and some advanced artificial intelligence techniques, European scientists have been counting the trees in a parched African desert. They pored over 1.3 million square kilometres of the waterless western Sahara and the arid lands of the Sahel to the south, to identify what is in effect an unknown forest. This region a stretch of dunes and dryland larger than Angola, or Peru, or Niger proved to be home to 1.8 billion trees and shrubs with crowns larger than three square metres. "We were very surprised to see that quite a few trees actually grow in the Sahara Desert because up till now, most people thought that virtually none existed. We counted hundreds of millions of trees in the desert alone," said Martin Brandt, a geographer at the University of Copenhagen in Denmark, who led the research.

Over the last year, we saw a greater shift towards automation and AI applications to streamline insurance, including increased usage of augmented reality to support activities ranging from warning of risks, explaining insurance plans, estimating damages and increasing brand awareness. We also saw insurers starting to explore greater use of blockchain, the tech behind cryptocurrencies, to better support operations. With this came a greater emphasis on cybersecurity, with the expectation for more proactive and preventative measures. As we enter the next decade, we'll continue to see unprecedented growth in innovation in the Insurtech space, which has set up the industry for more market advancements in an increasingly complex environment. The Canadian insurance industry has been largely inert and less agile in the past, and it's this environment where Insurtech has made its mark.

Pathfinding in Euclidean space is a common problem faced in robotics and computer games. For long-distance navigation on the surface of the earth or in outer space however, approximating the geometry as Euclidean can be insufficient for real-world applications such as the navigation of spacecraft, aeroplanes, drones and ships. This article describes an any-angle pathfinding algorithm for calculating the shortest path between point pairs over the surface of a sphere. Introducing several novel adaptations, it is shown that Anya as described by (Harabor & Grastien, 2013) for Euclidean space can be extended to Spherical geometry. There, where the shortest-distance line between coordinates is defined instead by a great-circle path, the optimal solution is typically a curved line in Euclidean space. In addition the turning points for optimal paths in Spherical geometry are not necessarily corner points as they are in Euclidean space, as will be shown, making further substantial adaptations to Anya necessary. Spherical Anya returns the optimal path on the sphere, given these different properties of world maps defined in Spherical geometry. It preserves all primary benefits of Anya in Euclidean geometry, namely the Spherical Anya algorithm always returns an optimal path on a sphere and does so entirely on-line, without any preprocessing or large memory overheads. Performance benchmarks are provided for several game maps including Starcraft and Warcraft III as well as for sea navigation on Earth using the NOAA bathymetric dataset. Always returning the shorter path compared with the Euclidean approximation yielded by Anya, Spherical Anya is shown to be faster than Anya for the majority of sea routes and slower for Game Maps and Random Maps.

Navigation is an essential ability for mobile agents to be completely autonomous and able to perform complex actions. However, the problem of navigation for agents with limited (or no) perception of the world, or devoid of a fully defined motion model, has received little attention from research in AI and Robotics. One way to tackle this problem is to use guided navigation, in which other autonomous agents, endowed with perception, can combine their distinct viewpoints to infer the localisation and the appropriate commands to guide a sensory deprived agent through a particular path. Due to the limited knowledge about the physical and perceptual characteristics of the guided agent, this task should be conducted on a level of abstraction allowing the use of a generic motion model, and high-level commands, that can be applied by any type of autonomous agents, including humans. The main task considered in this work is, given a group of autonomous agents perceiving their common environment with their independent, egocentric and local vision sensors, the development and evaluation of algorithms capable of producing a set of high-level commands (involving qualitative directions: e.g. move left, go straight ahead) capable of guiding a sensory deprived robot to a goal location.